A new trapdoor spider of Cyclocosmia Ausserer, 1871 from southern China (Araneae, Halonoproctidae)

The genus Cyclocosmia Ausserer, 1871 previously included ten species from North America and Asia, six of which have been recorded from China.A new species, Cyclocosmia ruyi Yu & Zhang sp. n., is described and diagnosed, based on both sexes from Guangxi Province, China. Morphological characters for the early stages of juveniles of the new species are also provided

SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT

Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are too simplistic, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption

Prognostic and therapeutic significance of microbial cell-free DNA in plasma of people with acutely decompensated cirrhosis

BACKGROUND AND AIMS: Although the effect of bacterial infection on cirrhosis has been well-described, the effect of non-hepatotropic virus (NHV) infection is unknown. This study evaluated the genome fragments of circulating microorganisms using metagenomic next-generation sequencing (mNGS) in cirrhosis patients with acute decompensation (AD), focusing on NHVs and related the findings to clinical outcomes. METHODS: Plasma mNGS was performed in 129 cirrhosis patients with AD in study cohort. Ten healthy volunteers and 20, 39, and 81 patients with stable cirrhosis, severe sepsis and hematological malignancies, respectively, were enrolled as controls. Validation assays for human cytomegalovirus (CMV) reactivation in a validation cohort (n = 58) were performed and exploratory treatment instituted. RESULTS: In study cohort, 188 microorganisms were detected in 74.4% (96/129) patients, including viruses (58.0%), bacteria (34.1%), fungi (7.4%) and chlamydia (0.5%). Patients with AD had an NHV signature, and CMV was the most frequent NHV, which correlated with the clinical effect of empirical antibiotic treatment, progression to acute-on-chronic liver failure (ACLF), and 90-day mortality. The NHV signature in ACLF patients was similar to patients with sepsis and hematological malignancies. The treatable NHV, CMV was detected in 24.1% (14/58) patients in the validation cohort. Of the 14 cases with detectable CMV by mNGS, 9 were further validated by DNA RT-PCR or pp65 antigenemia testing. Three patients with CMV reactivation received ganciclovir therapy in exploratory manner with clinical resolutions. CONCLUSIONS: The results of this study suggests that NHVs may have a pathogenic role in complicating the course of AD. Further validation is needed to define whether this should be incorporated in the routine management of AD patients. IMPACT AND IMPLICATIONS: ●Cirrhosis patients with acute decompensation have a non-hepatotropic virus (NHV) signature, which is similar to that in sepsis and hematological malignancies patients. ●The detected viral signature had clinical correlates, including clinical efficacy of empirical antibiotic treatment, progression to acute-on-chronic liver failure and short-term mortality. ●The treatable NHV, CMV reactivation may be involved in the clinical outcomes of decompensated cirrhosis. ●Routine screening for NHVs, especially CMV, may be useful for the management of patients with acutely decompensated cirrhosis

A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach

Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%

A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios

Implicit Neural Mapping for a Data Closed-Loop Unmanned Aerial Vehicle Pose-Estimation Algorithm in a Vision-Only Landing System

Due to their low cost, interference resistance, and concealment of vision sensors, vision-based landing systems have received a lot of research attention. However, vision sensors are only used as auxiliary components in visual landing systems because of their limited accuracy. To solve the problem of the inaccurate position estimation of vision-only sensors during landing, a novel data closed-loop pose-estimation algorithm with an implicit neural map is proposed. First, we propose a method with which to estimate the UAV pose based on the runway’s line features, using a flexible coarse-to-fine runway-line-detection method. Then, we propose a mapping and localization method based on the neural radiance field (NeRF), which provides continuous representation and can correct the initial estimated pose well. Finally, we develop a closed-loop data annotation system based on a high-fidelity implicit map, which can significantly improve annotation efficiency. The experimental results show that our proposed algorithm performs well in various scenarios and achieves state-of-the-art accuracy in pose estimation

A Filter Pruning Method of CNN Models Based on Feature Maps Clustering

The convolutional neural network (CNN) has been widely used in the field of self-driving cars. To satisfy the increasing demand, the deeper and wider neural network has become a general trend. However, this leads to the main problem that the deep neural network is computationally expensive and consumes a considerable amount of memory. To compress and accelerate the deep neural network, this paper proposes a filter pruning method based on feature maps clustering. The basic idea is that by clustering, one can know how many features the input images have and how many filters are enough to extract all features. This paper chooses Retinanet and WIDER FACE datasets to experiment with the proposed method. Experiments demonstrate that the hierarchical clustering algorithm is an effective method for filtering pruning, and the silhouette coefficient method can be used to determine the number of pruned filters. This work evaluates the performance change by increasing the pruning ratio. The main results are as follows: Firstly, it is effective to select pruned filters based on feature maps clustering, and its precision is higher than that of a random selection of pruned filters. Secondly, the silhouette coefficient method is a feasible method for finding the best clustering number. Thirdly, the detection speed of the pruned model improves greatly. Lastly, the method we propose can be used not only for Retinanet, but also for other CNN models. Its effect will be verified in future work

UDP-glucose pyrophosphorylase as a target for regulating carbon flux distribution and antioxidant capacity in Phaeodactylum tricornutum

Abstract UDP-glucose pyrophosphorylase (UGPase) is a key enzyme for polysaccharide synthesis, and its role in plants and bacteria is well established; however, its functions in unicellular microalgae remain ill-defined. Here, we perform bioinformatics, subcellular localization as well as in vitro and in vivo analyses to elucidate the functions of two UGPs (UGP1 and UGP2) in the model microalga Phaeodactylum tricornutum. Despite differences in amino acid sequence, substrate specificity, and subcellular localization between UGP1 and UGP2, both enzymes can efficiently increase the production of chrysolaminarin (Chrl) or lipids by regulating carbon flux distribution without impairing growth and photosynthesis in transgenic strains. Productivity evaluation indicate that UGP1 play a bigger role in regulating Chrl and lipid production than UGP2. In addition, UGP1 enhance antioxidant capacity, whereas UGP2 is involved in sulfoquinovosyldiacylglycerol (SQDG) synthesis in P. tricornutum. Taken together, the present results suggest that ideal microalgal strains can be developed for the industrial production of Chrl or lipids and lay the foundation for the development of methods to improve oxidative stress tolerance in diatoms